Cross-platform workload processing

ABSTRACT

According to one aspect of the present disclosure, a method and technique for workload processing is disclosed. The method includes: receiving a request to process a workload by a scheduler executing on a processor unit; accessing historical processing data by the scheduler to determine execution statistics associated with previous processing requests; determining whether the data of the workload is available for processing; in response to determining that the data is available for processing, determining whether a process for the workload is available; in response to determining that the process is available, determining resource availability on a computing platform for processing the workload; determining whether excess capacity is available on the computing platform based on the resource availability and the execution statistics; and in response to determining that excess capacity exists on the computing platform, initiating processing of the workload on the computing platform.

BACKGROUND

One goal in distributed and other types of computing environments is tomaximize throughput of large numbers of related workloads. One techniqueused to address the foregoing is time-based scheduling (i.e., schedulingthe times when each of the workloads should execute). Another techniqueis sequence-based scheduling (i.e., scheduling a sequence in which theworkloads should execute). Priority-based scheduling may also be used(i.e., running the workloads based on CPU availability and workloadpriority). Despite these techniques, there may be delays or otherconditions that may adversely affect throughput of the workload.

BRIEF SUMMARY

According to one aspect of the present disclosure a method and techniquefor cross-platform workload processing is disclosed. The methodincludes: receiving a request to process a workload by a schedulerexecuting on a processor unit; accessing historical processing data bythe scheduler to determine execution statistics associated with previousprocessing requests; determining whether the data of the workload isavailable for processing; in response to determining that the data isavailable for processing, determining whether a process for the workloadis available; in response to determining that the process is available,determining resource availability on a computing platform for processingthe workload; determining whether excess capacity is available on thecomputing platform based on the resource availability and the executionstatistics; and in response to determining that excess capacity existson the computing platform, initiating processing of the workload on thecomputing platform.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

For a more complete understanding of the present application, theobjects and advantages thereof, reference is now made to the followingdescriptions taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is an embodiment of a network of data processing systems in whichthe illustrative embodiments of the present disclosure may beimplemented;

FIG. 2 is an embodiment of a data processing system in which theillustrative embodiments of the present disclosure may be implemented;

FIG. 3 is a diagram illustrating an embodiment of a computingenvironment in which illustrative embodiments of a cross-platformworkload processing system according to the present disclosure may beimplemented;

FIG. 4 is a diagram illustrating an embodiment of cross-platformworkload processing according to the present disclosure;

FIG. 5 is a diagram illustrating another embodiment of cross-platformworkload processing according to the present disclosure; and

FIG. 6 is a flow diagram illustrating an embodiment of a method forcross-platform workload processing according to the present disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure provide a method, system andcomputer program product for cross-platform workload processing. Forexample, in some embodiments, the method and technique includes:receiving a request to process a workload by a scheduler executing on aprocessor unit; accessing historical processing data by the scheduler todetermine execution statistics associated with previous processingrequests; determining whether the data of the workload is available forprocessing; in response to determining that the data is available forprocessing, determining whether a process for the workload is available;in response to determining that the process is available, determiningresource availability on a computing platform for processing theworkload; determining whether excess capacity is available on thecomputing platform based on the resource availability and the executionstatistics; and in response to determining that excess capacity existson the computing platform, initiating processing of the workload on thecomputing platform. Thus, embodiments of the present disclosure enableworkload throughput optimization using a knowledge base to evaluatethroughput information obtained from previous, similar workloadprocessing to efficiently schedule and manage workload processing acrossone or more computing platforms. A set of rules may be set/used todetermine which workload (input data and process) is to be processednext, the availability of the workload, and on a priority determined bythe knowledge base. A scheduler waits for the arrival/availability ofprocesses and their related data and, in response to the data and itsassociated process(es) having arrived or otherwise being available, theworkload is eligible to be submitted according to a priority of theworkload (e.g., as set forth by the rules) and the availability ofprocessing/execution resources. The scheduler controls/managesintermediate processing stages of the workload without user interventionbased on the workload being available for processing as well ascomputing resource availability.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer usable or computer readablemedium(s) may be utilized. The computer readable medium may be acomputer readable signal medium or a computer readable storage medium. Acomputer readable storage medium may be, for example but not limited to,an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

With reference now to the Figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments of the present disclosure maybe implemented. It should be appreciated that FIGS. 1-2 are onlyexemplary and are not intended to assert or imply any limitation withregard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may bemade.

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments of the present disclosure maybe implemented. Network data processing system 100 is a network ofcomputers in which the illustrative embodiments of the presentdisclosure may be implemented. Network data processing system 100contains network 130, which is the medium used to provide communicationslinks between various devices and computers connected together withinnetwork data processing system 100. Network 130 may include connections,such as wire, wireless communication links, or fiber optic cables.

In some embodiments, server 140 and server 150 connect to network 130along with data store 160. Server 140 and server 150 may be, forexample, IBM® Power Systems™ servers. In addition, clients 110 and 120connect to network 130. Clients 110 and 120 may be, for example,personal computers or network computers. In the depicted example, server140 provides data and/or services such as, but not limited to, datafiles, operating system images, and applications to clients 110 and 120.Network data processing system 100 may include additional servers,clients, and other devices.

In the depicted example, network data processing system 100 is theInternet with network 130 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation for thedifferent illustrative embodiments.

FIG. 2 is an embodiment of a data processing system 200 such as, but notlimited to, client 110 and/or server 140 in which an embodiment of asystem for cross-platform workload processing according to the presentdisclosure may be implemented. In this embodiment, data processingsystem 200 includes a bus or communications fabric 202, which providescommunications between processor unit 204, memory 206, persistentstorage 208, communications unit 210, input/output (I/O) unit 212, anddisplay 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

In some embodiments, memory 206 may be a random access memory or anyother suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. Persistent storage 208 may be a hard drive,a flash memory, a rewritable optical disk, a rewritable magnetic tape,or some combination of the above. The media used by persistent storage208 also may be removable such as, but not limited to, a removable harddrive.

Communications unit 210 provides for communications with other dataprocessing systems or devices. In these examples, communications unit210 is a network interface card. Modems, cable modem and Ethernet cardsare just a few of the currently available types of network interfaceadapters. Communications unit 210 may provide communications through theuse of either or both physical and wireless communications links.

Input/output unit 212 enables input and output of data with otherdevices that may be connected to data processing system 200. In someembodiments, input/output unit 212 may provide a connection for userinput through a keyboard and mouse. Further, input/output unit 212 maysend output to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer readable media 218 form computerprogram product 220 in these examples. In one example, computer readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 218 is also referred to as computerrecordable storage media. In some instances, computer readable media 218may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown. For example, a storage device indata processing system 200 is any hardware apparatus that may storedata. Memory 206, persistent storage 208, and computer readable media218 are examples of storage devices in a tangible form.

FIG. 3 is a diagram illustrating a computing environment in which anembodiment of a system 300 for cross-platform workload processingaccording to the present disclosure may be implemented. System 300enables the throughput maximization of very large volumes of workloadsusing knowledge-based or learned information. The workloads may includedata and their associated processes (programs/applications that willprocess the data). The data may be processed multiple times across oneor more different computing platforms before the final output iscreated. The workload throughput is controlled by a process schedulerthat maximizes the throughput of the workloads through one or morecomputing platforms without human/user intervention/interaction. In theillustrated embodiment, system 300 includes a host 302 such as, but notlimited to, client 110 and/or server 140, having a processor unit 310and a memory 312. In FIG. 3, memory 312 includes a scheduler 314 forscheduling and performing other actions relative to workload processingas discussed below and a knowledge base 316. Scheduler 314 may beimplemented in any suitable manner using known techniques that may behardware-based, software-based, or some combination of both. Forexample, scheduler 314 may comprise software, logic and/or executablecode for performing various functions as described herein (e.g.,residing as software and/or an algorithm running on a processor unit,hardware logic residing in a processor or other type of logic chip,centralized in a single integrated circuit or distributed amongdifferent chips in a data processing system).

In the illustrated embodiment, a workload may be considered as a looselycoupled object in an object oriented system. For example, a workload maycomprise data and the processes needed to process that data. Scheduler314 utilizes global variables (including information from knowledge base316) in order to control the scheduling and processing of workloads.Each processing event requires that the data and the processes for thatevent be available. In the illustrated embodiment, knowledge base 316comprises a set of rules 320 and processing history data 322. Processinghistory data 322 may comprise information learned/gained from previousexecutions/processing of the same data types by the same processes. Forexample, computing resources needed to process the data include the CPUas well as many other resources (e.g., memory availability, diskinput/output (I/O), network I/O, other hardware and operating systemresource constraints, etc.). Scheduler 314 “learns” from previousprocess executions for the same data type by utilizing a simple proxyfor the overall throughput of the workload. The throughput for aparticular data type may be determined using the following:

Throughput(dataType)=Elapsed time/MB=CPU time/MB+Other time/MB

where the “other time” may be the summation of all times required forexecution as the result of the availability of other computingresources, and MB is a megabyte of data.

Rules 320 may comprise information or rules that are created andmaintained external to the programs/processes acting on the data (e.g.,in a separate text file), thereby enabling rules 320 to be readilychanged as needed. Rules 320 may comprise information and/or rules thattake into account the various types of resources available (e.g., numberof CPUs, CPU utilization, disk space requirements, disk I/O, networkI/O, etc.), the “throughput” of previous processes of the same type as apredictor for the required resources for the next process of the sametype, and data type default assumptions. In some embodiments, rules 320may be “implementation defined” such as based on a particular data type,user/workload priority, etc. Some example rules 320 may be:

-   -   dataTypeP is a heavy CPU utilizer, so only execute a maximum of        one process per CPU;    -   dataTypeT is a light CPU utilizer, so only execute a maximum of        five processes per CPU;    -   dataTypeT is a light CPU utilizer and there are no dataTypeP        processes running, so execute a maximum of tem processes CPU;        and    -   if userID=xx, then priority is high.

In the illustrated embodiment, system 300 includes one or more computingplatforms 330 for performing process operations on workloads. In FIG. 3,three computing platforms 330 ₁, 330 ₂ and 330 ₃ are depicted; however,it should be understood that a greater or fewer quantity of computingplatforms 330 may be used. Further, it should be understood thatworkload processing may also be performed on host 302 using resourcesavailable on host 302. Each computing platform 330 may include one ormore processor units 332 ₁₋₃, a memory 334 ₁₋₃ having one or moreinstances of an operating system 336 ₁₋₃ and one or moreprograms/applications 338 ₁₋₃ for performing corresponding processes 340₁₋₃ on data. In the illustrated embodiment, memory 312 of host 302 mayalso include resource data 324 comprising information associated withcomputing resources available for processing workloads (e.g., computingresources of platforms 330).

From an operational perspective, data and processes are “fed” into thesystem by placing them in “input source” locations. An input sourcelocation may comprise a “data source” location and a “process source”location. For example, placing the data and/or processes in input sourcelocations may comprise pacing them in input queues (e.g., where eachqueue contains data or processes of a specific workload type) or placingthem in input directories (e.g., where each directory contains data orprocesses of a specific workload type). Both “input queues” and “inputdirectories” may be considered logically equivalent sources or feedssince in either case the availability of data and/or processes is madeknown to scheduler 314 by placing the data in a known input location.The processes may also be received in an input queue, thereby enabling adifferent process to be used for processing the same data type based onsome requirement that is determined external to scheduler 314. Also thesame process may be used to process the particular data type but theparameters that are submitted along with the process may vary based onthe particular instance of the data type. These parameters may be passedas part of a process execution string or passed in as a batch job (e.g.,an execution script or its equivalent JCL (Job Control Language) formainframe execution). In the illustrated embodiment, processes and theprograms/application associated with such processes are depicted asresiding on particular platforms 330; however, it should be understoodthat the data and/or processes (e.g., instances of applications/programsfor processing the data) may loaded and/or instantiated on a particularcomputing platform 330 as such instances become available (e.g., due tolicensing constraints, current use on other platforms, etc.).

Scheduler 314 is configured to select data from the appropriate datasource for processing based on knowledge base 316 and the availablecomputing resources. Processing of the data may involve running multipleprocesses that either consume data from multiple sources or use theoutput of one process as the input to the next process, or a combinationof both. In the final step the output data may be placed into a “datadestination” (e.g., an output queue or output directory). For example,in the illustrated embodiment, input data 342 may, in response tobecoming available for processing, be processed by computing platform330 ₁ via process 340 ₁, and the output data 344 of that process, whenavailable, becomes input data 346 to be processed by computing platform330 ₂ via process 340 ₂. In turn, output data 348 from process 340 ₂becomes input data 350 to be processed by computing platform 330 ₃ viaprocess 340 ₃. Output data 352 of process 340 ₃ may be provided toanother computing platform 330, placed in an output queue or outputdirectory, or otherwise managed/processed. It should be understood thatmultiple processes may be performed by a single computing platform 330.

Scheduler 314 controls and/or manages workload processing based on whenthe data is received/available such that the process becomes eligible toproceed when the data is available. Scheduler 314 may initiate aworkload processing operation based on rules 320, history data 322,workload/data priority and the computing resources available. When anoutput from a first process is completed, the next process is theneligible to proceed to process the data. This process may be repeateduntil the desired output is reached. Processing on multiple platforms330 is enabled by controlling the input and output data and/or processlocations, thereby enabling multiple platforms 330 to be daisy-chainedor linked together for workload processing. In some embodiments, a datapush scenario may be utilized (e.g., output data from one computingplatform 330 is transmitted to another computing platform 330 furtherprocessing). The determination of which platform 330 to transmit to canbe predetermined based on data type, predetermined based on processtype, based on a round robin type rule. In some embodiments, a data pullscenario may be utilized (e.g., the final output data may remain on onecomputing platform 330 while other computing platform(s) 330 may pollthe first platform 330 for data availability as resource availabilitypermits). For example, the output data 344 may remain on platform 330 ₁and platforms 330 ₂ and 330 ₃ may poll platform 3301 for dataavailability as resources on the respective platforms 330 ₂ and 330 ₃become available (i.e., whichever of the two platforms 330 ₂ and 330 ₃is least busy will pick-up the data from platform 330 ₁).

FIG. 4 is a diagram illustrating an embodiment of cross-platformworkload processing according to the present disclosure. In theillustrated embodiment, a batch migration is depicted where a largerepository (e.g., multiple terabytes or greater) of data is to bemigrated from one archive/storage system to another (or from one mediatype or computing platform to another). The migration process isachieved by creating and executing thousands through tens of thousandsof batch jobs (in this example, each batch job is a script thatinitiates a single process). In this example, the processing of the datapasses through multiple states. There are two general states (e.g., astate in which the data is “at rest” (e.g., ready, residing on disk) anda second general state when a process is running against the data toproduce an output dataset). In this example, a single input data setgoes through multiple processing steps before the final output isproduced. Each of these processing steps utilizes different resourcesbased on the functionality of the process (e.g., a compression processwould consume mainly CPU and memory while a file transfer process wouldconsume mainly network bandwidth). There are multiple of these processesexecuting concurrently. Scheduler 314 selects which processes toactivate/initiate based on data and process availability as well assystem resource availability and process performance based on historicaldata collection.

In the illustrated example, multiple processes 402 may need to beperformed on input data 404 during the migration process. Each process402 may be performed on a different computing platform 330 or multipleprocesses 402 may be performed on a single platform 330, or somecombination thereof. Scheduler 314 controls/manages the workloadprocessing through a series of operations. Scheduler 314 may firstdetermine whether and/or wait for data 404 and its associated process(e.g., process 1 402 ₁) to become available at a source (e.g., an inputqueue 406). In response to data 404 and processes 402 ₁ becomingavailable for input, scheduler 314 observes and/or otherwise determinesany currently executing workloads and the available computing resources(e.g., resource data 324) of computing platform(s) 330. If excessresource capacity is available, scheduler 314 examines knowledge base316 and decides if the available workload (e.g., data 404 and process402 ₁) can be run at this time without over-utilizing (degrading) any ofthe computing resources on platform(s) 330. If so, scheduler 314 selectsthe data 404 and the corresponding resource (e.g., one or more ofplatforms 330) for execution of the workload and initiates theprocessing of the workload on the selected resource (e.g., state 1). Atstate 2, process 402 ₁ has been completed and the processed data mayreside on a disk/storage resource or be otherwise at rest awaiting anext processing operation (e.g., in an output queue or outputdirectory). The above process is then repeated by scheduler 314 atstates 3 for a process 402 ₂, and state 4 through state N, until data404 has completed its final processing operations, where output data 410may be stored and/or further managed. Further, scheduler 314 determinesand/or otherwise obtains the processing/execution statistics associatedwith workload processing and updates processing history data 322 (e.g.,based on the data type, process, etc.).

FIG. 5 is a diagram illustrating another embodiment of cross-platformworkload processing according to the present disclosure. In thisembodiment, multiple dataset processing where streams of input data maybe continually processed is depicted. In this example, rules 320 may besetup/configured to select which of the input data streams would beprocessed first, second, etc. This prioritization may be based on datatype, data and process availability, or any other rule defined for theenvironment. In this example, one or more processes 502 require inputfrom two or more datasets. For example, in the illustrated embodiment,input data 504 will be processed via a process 502 ₁. Scheduler 314 willwait until all the needed datasets are available for process 502 ₂(e.g., input data 506 and input data 504 that has been processed viaprocess 502 ₁) before process 502 ₂ becomes eligible to proceed. Inresponse to input data 404 becoming available after process 502 ₁ andinput data 506 being available, along with the program/process 502 ₂,scheduler 314 will initiate the workload processing operation (e.g.,process 502 ₂) process based on information in knowledge base 316 andresource availability (e.g., using resource data 324). Furtherprocessing of workloads through various states or stages may be as setforth above.

Thus, in operation, rules 320 are used to determine which workload(input data and process) is to be processed next. The rules 320 are usedto select the data based on the availability of the workload (processesand data) and on a priority determined by knowledge base 316. Scheduler314 waits for the arrival and/or availability of processes and theirrelated data. Once both the process and its data are available, theworkload is eligible to be submitted/initiated/processed. Rules 320 mayalso be used to determine the priority of the workload submission. Theavailability of processing/execution resources determines when theworkload is executed. Each dataset/input data may go through variousindependent but sequentially related processes before the final outputis produced, and multiple datasets may be included within a singleprocessing operation. Scheduler 314 controls the intermediate processingoperations, the combining of datasets for processing when needed, andthe combining of the output of different processes into the input foranother, subsequent process. Scheduler 314 also manages/controlsworkload processing across computing platforms of varying technologies(e.g., different types of operating systems, different data formats,etc.).

FIG. 6 is a flow diagram illustrating an embodiment of a method forcross-platform workload processing according to the present disclosure.The method begins at block 602, where scheduler 314 receives a workloadprocessing request (e.g., a request to migrate data from one platform toanother, from one data type to another, etc.). At block 604, scheduler314 may determine needed processes for the workload processing. Forexample, scheduler 314 may access knowledge base 316 to determine thetypes of processes for processing the workload (e.g., data compressionor extraction, translation, etc.). At block 606, scheduler 314 mayaccesses knowledge base 316 to determine computing resources needed forprocessing the workload. For example, scheduler 314 may access rules 320or other types of information in knowledge base to identify a particularprogram/application/process (or processes needed at various stages ofprocessing the workload) (e.g., compression process, followed by atranslation process, etc.). At decisional block 608, scheduler 314determines whether the data corresponding to the workload is available(e.g., available at a particular source, queue or directory). If not,the method proceeds to block 610, where scheduler 314 may continue tomonitor for the availability of the workload data.

In response to the data becoming available, the method proceeds fromdecisional block 608 to decisional block 612, where scheduler 314determines whether the process for the workload is available. If not,the method proceeds to block 614, where scheduler 314 may continue tomonitor for the availability of the process. In response to the processbecoming available, the method proceeds from decisional block 612 toblock 616, where scheduler 314 observes current computing processinglevels (e.g., current workload processing levels of computing platforms330). At block 618, scheduler 314 determines and/or otherwise identifiesavailable computing resources for the workload processing on computingplatforms 330 (e.g., accessing and/or reviewing resource data 324 orotherwise communicating with computing platforms 330 to assess currentprocessing levels). At decisional block 620, scheduler 314 determineswhether excess capacity exists on one or more computing platforms 330for processing the workload. For example, scheduler 314 may accessknowledge base 316 (e.g., processing history data 322) to determine anumber of CPUs, memory capacity, etc., needed for processing theworkload to ensure resources of the computing platform are notover-utilized. If insufficient capacity is determined, the methodproceeds to block 622, where scheduler 314 may continue to monitorprocessing levels of the available computing platforms.

If excess capacity is identified at decisional block 620, the methodproceeds to block 624, where scheduler 314 may access rules 320 forevaluating a priority of the workload relative to other workloadsneeding to be processed. At decisional block 626, scheduler 314determines whether initiating processing of the workload complies withrules 320 (e.g., priority levels, needed resources, etc.). If not, themethod proceeds to block 628, where scheduler 314 may prioritize theworkload according to rules 320. If a determination is made atdecisional block 626 that the workload meets the priority requirement orthat the priority of the workload is such that it exceeds other waitingworkloads, the method proceeds to block 630, where scheduler 314initiates and/or otherwise causes execution of the workload processingusing the selected computing platform. At block 632, scheduler 314access processing statistics associated with the processing of theworkload (e.g., throughput information, memory capacity utilized, etc.)and updates processing history data 324.

Thus, embodiments of the present disclosure enable workload throughputoptimization using a knowledge base to evaluate throughput informationobtained from previous, similar workload processing to efficientlyschedule and manage workload processing across one or more computingplatforms. A set of rules may be set/used to determine which workload(input data and process) is to be processed next, the availability ofthe workload, and a priority determined by the knowledge base. Ascheduler waits for the arrival/availability of processes and theirrelated data and, in response to the data and its associated process(es)having arrived or otherwise being available, the workload is eligible tobe submitted according to a priority of the workload (e.g., as set forthby the rules) and the availability of processing/execution resources.The scheduler controls/manages intermediate processing stages of theworkload without user intervention based on the workload being availablefor processing as well as computing resource availability.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method, comprising: receiving a request toprocess a workload by a scheduler executing on a processor unit;accessing historical processing data by the scheduler to determineexecution statistics associated with previous processing requests;determining whether the data of the workload is available forprocessing; in response to determining that the data is available forprocessing, determining whether a process for the workload is available;in response to determining that the process is available, determiningresource availability on a computing platform for processing theworkload; determining whether excess capacity is available on thecomputing platform based on the resource availability and the executionstatistics; and in response to determining that excess capacity existson the computing platform, initiating processing of the workload on thecomputing platform.
 2. The method of claim 1, further comprising:determining workload processing statistics of the workload processing;and updating the execution statistics with the workload processingstatistics.
 3. The method of claim 1, further comprising: accessing aset of rules indicating resource utilization for a plurality ofdifferent types of workloads; and determining the resource utilizationfrom the rules for processing the workload.
 4. The method of claim 1,wherein accessing historical processing data comprises determiningexecution statistics for a same data type as the data of the workload.5. The method of claim 1, wherein accessing historical processing datacomprises determining data throughput statistics for a same type of dataas the data of the workload.
 6. The method of claim 1, wherein receivingthe request comprises: determining a first workload process for theworkload, the first workload process processing the data from a firststate to a second state; automatically initiating the first workloadprocess on a first computing platform in response to the data in thefirst state being available to the first computing platform; determininga second workload process for the workload, the second workload processprocessing the data from the second state to a third state; andautomatically initiating the second workload process on a secondcomputing platform in response to the data in the second state beingavailable to the second computing platform.
 7. The method of claim 6,further comprising polling, by the second computing, the first computingplatform to determine whether the data in the second state is available.